标题： 使用大型语言模型进行临床笔记的高效标准化 显示英文标题

标题： Efficient Standardization of Clinical Notes using Large Language Models

摘要： 临床医生的笔记是患者信息的丰富来源，但由于不同的书写风格、口语表达、缩写、医学术语、语法错误和非标准格式，常常包含不一致之处。 这些不一致之处阻碍了从电子健康记录（EHR）中提取有意义的数据，对质量改进、人群健康、精准医学、决策支持和研究构成了挑战。 我们提出了一种大型语言模型方法，用于标准化1,618份临床笔记的语料库。 标准化平均纠正了$4.9 +/- 1.8$个语法错误，$3.3 +/- 5.2$个拼写错误，将$3.1 +/- 3.0$个非标准术语转换为标准术语，并每篇笔记扩展了$15.8 +/- 9.1$个缩写和首字母缩略词。 此外，笔记被重新组织成具有标准化标题的规范部分。 这一过程使笔记适用于关键概念提取、映射到医学本体论以及转换为可互操作的数据格式，如FHIR。 随机抽样的笔记专家评审未发现标准化后有显著的数据丢失。 这项概念验证研究证明，临床笔记的标准化可以提高其可读性、一致性和可用性，同时也有助于将其转换为可互操作的数据格式。 显示英文摘要

摘要： Clinician notes are a rich source of patient information but often contain inconsistencies due to varied writing styles, colloquialisms, abbreviations, medical jargon, grammatical errors, and non-standard formatting. These inconsistencies hinder the extraction of meaningful data from electronic health records (EHRs), posing challenges for quality improvement, population health, precision medicine, decision support, and research. We present a large language model approach to standardizing a corpus of 1,618 clinical notes. Standardization corrected an average of $4.9 +/- 1.8$ grammatical errors, $3.3 +/- 5.2$ spelling errors, converted $3.1 +/- 3.0$ non-standard terms to standard terminology, and expanded $15.8 +/- 9.1$ abbreviations and acronyms per note. Additionally, notes were re-organized into canonical sections with standardized headings. This process prepared notes for key concept extraction, mapping to medical ontologies, and conversion to interoperable data formats such as FHIR. Expert review of randomly sampled notes found no significant data loss after standardization. This proof-of-concept study demonstrates that standardization of clinical notes can improve their readability, consistency, and usability, while also facilitating their conversion into interoperable data formats.